Automatic Domain Identification for Linked Open Data

Lalithsena, Sarasi; Hitzler, Pascal; Sheth, Amit P.; Jain, Prateek

doi:10.1109/wi-iat.2013.206

Cited by 21 publications

(21 citation statements)

References 21 publications

(33 reference statements)

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“…However, there has been lately a plethora of tools like ExpLOD [8], LODStats [2], ProLOD++ [1], LODOP [6] and Aether [12] that compute some statistical information which is vital to applications dealing with query optimization and answering, data cleansing, schema induction and data mining [7,9].…”

Section: Related Workmentioning

confidence: 99%

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Roomba

Assaf

Senart

Troncy

2015

Proceedings of the 24th International Conference on World Wide Web

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Data is being published by both the public and private sectors and covers a diverse set of domains ranging from life sciences to media or government data. An example is the Linked Open Data (LOD) cloud which is potentially a gold mine for organizations and individuals who are trying to leverage external data sources in order to produce more informed business decisions. Considering the significant variation in size, the languages used and the freshness of the data, one realizes that spotting spam datasets or simply finding useful datasets without prior knowledge is increasingly complicated. In this paper, we propose Roomba, a scalable automatic approach for extracting, validating, correcting and generating descriptive linked dataset profiles. While Roomba is generic, we target CKAN-based data portals and we validate our approach against a set of open data portals including the Linked Open Data (LOD) cloud as viewed on the DataHub. The results demonstrate that the general state of various datasets and groups, including the LOD cloud group, needs more attention as most of the datasets suffer from bad quality metadata and lack some informative metrics that are required to facilitate dataset search.

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Section: Related Workmentioning

confidence: 99%

“…Topical profiling focuses on content-wise analysis at the instances and ontological levels. In [9,3,5], the authors try to automatically infer information about the underlying dataset depending on external knowledge bases or the dataset's internal structure and ontological information.…”

Section: Related Workmentioning

confidence: 99%

Roomba

Assaf

Senart

Troncy

2015

Proceedings of the 24th International Conference on World Wide Web

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“…However, this approach expects that the data publishers or some third party provide the annotations. Lalithsena et al proposed an approach to automatically identify the domains of these datasets by utilizing Freebase, both as the background knowledge and as the vocabulary (Freebase domains and categories). This approach can be used to address the scalability issues in manual tagging of datasets of the aforementioned approaches.…”

Section: Data Source Selection For Querying On Lodmentioning

confidence: 99%

Alignment and dataset identification of linked data in Semantic Web

Gunaratna

Lalithsena

Sheth

2014

WIREs Data Min & Knowl

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The Linked Open Data (LOD) cloud has gained significant attention in the Semantic Web community over the past few years. With rapid expansion in size and diversity, it consists of over 800 interlinked datasets with over 60 billion triples. These datasets encapsulate structured data and knowledge spanning over varied domains such as entertainment, life sciences, publications, geography, and government. Applications can take advantage of this by using the knowledge distributed over the interconnected datasets, which is not realistic to find in a single place elsewhere. However, two of the key obstacles in using the LOD cloud are the limited support for data integration tasks over concepts, instances, and properties, and relevant data source selection for querying over multiple datasets. We review, in brief, some of the important and interesting technical approaches found in the literature that address these two issues. We observe that the general purpose alignment techniques developed outside the LOD context fall short in meeting the heterogeneous data representation of LOD. Therefore, an LOD specific review of these techniques (especially for alignment) is important to the community. The topics covered and discussed in this article fall under two broad categories, namely alignment techniques for LOD datasets and relevant data source selection in the context of query processing over LOD datasets.

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“…Diverse CKAN Extensions are being developed, and the system is applied in numerous fields for integrated management of open data. A typical example is the sharing of data produced in smart [13,14]. As a result of such efforts, there have been various systems capable of data integration and analysis using the CKAN.…”

Section: Introductionmentioning

confidence: 99%

A Study of Prescriptive Analysis Framework for Human Care Services Based On CKAN Cloud

2018

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A number of sensor devices are widely distributed and used today owing to the accelerated development of IoT technology. In particular, this technological advancement has allowed users to carry IoT devices with more convenience and efficiency. Based on the IoT sensor data, studies are being actively carried out to recognize the current situation or to analyze and predict future events. However, research for existing smart healthcare services is focused on analyzing users' behavior from single sensor data and is also focused on analyzing and diagnosing the current situation of the users. Therefore, a method for effectively managing and integrating a large amount of IoT sensor data has become necessary, and a framework considering data interoperability has become necessary. In addition, an analysis framework is needed not only to provide the analysis of the users' environment and situation from the integrated data, but also to provide guide information to predict future events and to take appropriate action by users. In this paper, we propose a prescriptive analysis framework using a 5W1H method based on CKAN cloud. Through the CKAN cloud environment, IoT sensor data stored in individual CKANs can be integrated based on common concepts. As a result, it is possible to generate an integrated knowledge graph considering interoperability of data, and the underlying data is used as the base data for prescriptive analysis. In addition, the proposed prescriptive analysis framework can diagnose the situation of the users through analysis of user environment information and supports users' decision making by recommending the possible behavior according to the coming situation of the users. We have verified the applicability of the 5W1H prescriptive analysis framework based on the use case of collecting and analyzing data obtained from various IoT sensors.

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Automatic Domain Identification for Linked Open Data

Cited by 21 publications

References 21 publications

Roomba

Roomba

Alignment and dataset identification of linked data in Semantic Web

A Study of Prescriptive Analysis Framework for Human Care Services Based On CKAN Cloud

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